class_docstr: Calculates the mean :class:`~torch.nn.PairwiseDistance`.
  Average of pairwise distances computed on provided batches.
  
  - ``update`` must receive output of the form ``(y_pred, y)``.
  
  Args:
      p: the norm degree. Default: 2
      eps: Small value to avoid division by zero. Default: 1e-6
      output_transform: a callable that is used to transform the
          :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
          form expected by the metric. This can be useful if, for example, you have a multi-output model and
          you want to compute the metric with respect to one of the outputs.
          By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
      device: specifies which device updates are accumulated on. Setting the
          metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
          non-blocking. By default, CPU.
  
  Examples:
      To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
      The output of the engine's ``process_function`` needs to be in the format of
      ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added
      to the metric to transform the output into the form expected by the metric.
  
      ``y_pred`` and ``y`` should have the same shape.
  
      For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
  
      .. include:: defaults.rst
          :start-after: :orphan:
  
      .. testcode::
  
          metric = MeanPairwiseDistance(p=4)
          metric.attach(default_evaluator, 'mpd')
          preds = torch.tensor([
              [1, 2, 4, 1],
              [2, 3, 1, 5],
              [1, 3, 5, 1],
              [1, 5, 1 ,11]
          ])
          target = preds * 0.75
          state = default_evaluator.run([[preds, target]])
          print(state.metrics['mpd'])
  
      .. testoutput::
  
          1.5955...
code: def update(self, output: Sequence[torch.Tensor]) -> None:
      y_pred, y = output[0].detach(), output[1].detach()
      distances = pairwise_distance(y_pred, y, p=self._p, eps=self._eps)
      self._sum_of_distances += torch.sum(distances).to(self._device)
      self._num_examples += y.shape[0]
  
file_path: ignite/ignite/metrics/mean_pairwise_distance.py
imports: from typing import Callable, Sequence, Union
  import torch
  from torch.nn.functional import pairwise_distance
  from ignite.exceptions import NotComputableError
  from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
label_desc: Use image node_id 3 for calling the MeanPairwiseDistance obj's underlying member method code with example usage: obj.update(output) without return types